A while ago I needed a resumable agent runtime.

I did not want something as large as Temporal, and I did not want another agent framework like LangChain. I wanted something small enough to understand, but solid enough to adapt across the different verticals I was building.

It started with a few bare-bones questions.

The moment an agent leaves a notebook, script, or chat session, the hard problems change:

• What work exists?
• Which worker owns it right now?
• What was the last durable step?
• Can another worker resume after a crash?
• Which resources are locked?
• What did the agent produce?
• Can operators inspect what happened?

The effect of it is Roost as a small runtime layer for that problem.

GitHub: https://github.com/mczaykowski/Roost

The basic idea

Roost treats an agent as a durable step machine.

An engine implements two methods:

class Engine:
    engine_id: str

    async def init_snapshot(self, item: WorkItem) -> Snapshot: ...
    async def step(self, snapshot: Snapshot, item: WorkItem) -> Snapshot: ...

The engine owns the domain-specific transition.

Roost owns the operational substrate:

Queue
  -> acquire lease
  -> load latest Snapshot
  -> Engine.step(snapshot, item)
  -> compare-and-swap save Snapshot
  -> re-enqueue or mark done

That gives you:

• durable snapshots
• per-work leases
• at-least-once execution
• retry-safe progress
• delayed continuation
• resource claims
• event history
• content-addressed artifacts
• failed-work inspection

It is intentionally small. It is not trying to be a prompt framework, model router, workflow DSL, or hosted agent platform.

Roost does not help an agent think.

Roost helps an agent keep going.

Why I built it

A lot of agent tooling focuses on the thinking loop: prompts, tools, retrieval, planning, memory, model routing.

That is useful, but once agents run as workers for minutes, hours, or days, the bottleneck becomes more boring and more operational.

For example:

• a worker dies halfway through a task
• the same job is delivered twice
• a long-running task needs to wait before its next step
• two workers should not touch the same resource at the same time
• an operator needs to know what happened
• the output needs to be inspectable later

You can solve this with a workflow engine, a custom queue, a database table, or a pile of scripts.

Roost is my attempt at a small, agent-shaped version of that layer.

A simple demo: crash-safe URL watchlist

The demo engine is a URL watchlist worker.

It fetches a URL over multiple steps, saves each observation into a snapshot, waits between checks, and writes a final JSON artifact.

You can kill the worker halfway through, restart it, and Roost resumes from the latest saved snapshot.

uv sync --extra redis --extra dev
docker run --rm -p 6379:6379 redis:7

In one terminal:

uv run roost worker --engines watchlist

In another:

WORK_ID=$(uv run roost enqueue \
  --engine watchlist \
  --resource domain:example.com \
  --payload '{"url":"https://example.com","claim":"Example Domain is reachable","checks_required":3,"delay_seconds":5}')

uv run roost status "$WORK_ID"

Then kill the worker with Ctrl-C, start it again, and inspect the same work item.

uv run roost worker --engines watchlist
uv run roost status "$WORK_ID"

There is also a local end-to-end script:

scripts/e2e_watchlist.sh

No LLM key is required. The demo is about runtime behavior, not model behavior.

Local console

Roost includes a small local console:

uv run roost ui

It shows live work, saved state, events, failed work, and artifacts.

The detail view lets you inspect payloads, snapshots, and outputs:

Where this fits

Roost is not a replacement for LangChain, LlamaIndex, CrewAI, AutoGen, Temporal, Celery, or your own agent loop.

It sits at a different layer.

LangChain helps decide what an agent should do.
Temporal helps coordinate workflows.
Celery runs jobs.
Roost keeps long-running agent workers alive, inspectable, and resumable.

The current backend is Redis + SAQ. Execution is at-least-once, so engines need to make step() retry-safe from the same snapshot.

That tradeoff is intentional. I would rather expose the semantics clearly than pretend exactly-once execution exists.

What I’m looking for feedback on

I’m especially interested in feedback on the abstraction boundary.

Is this useful as a small runtime under agent loops?

Would you rather reach for Temporal, Celery, or a custom queue?

Does the init_snapshot() / step() model feel too small, or exactly small enough?

GitHub: https://github.com/mczaykowski/Roost